In statistics, it is generally desirable to infer an important quantity from a small dataset. To compute bids that will result in high revenue for a given target return on advertising spending (ROAS), the principle quantities that are generally sought include a conversion rate and an average order value (AOV) for each ad. While current schemes may attempt to estimate these values in calculations, they typically invoke multifaceted penalties for slow or inefficient methods.
Consider an example in which the conversion rate estimate for an ad is too low. In such a case, one would run the risk that the correspondingly low bid would drop the ad out of circulation, thereby stopping data collection. Consider a less extreme situation in which a low bid could lower click traffic on the ad. Such a scenario would result in missed conversions. If a bid is too high for too long, it can result in prolonged and high levels of ad spend on a poorly performing ad.